Retrospective study of machine learning based Covid-19 prediction frameworks

During the deadly pandemic of COVID-19, several clinical and non-clinical mechanisms were adopted by the governments and World Health Organization to flatter the pandemic curve and succeed to a certain extent. Diversified research groups involved themselves in this mission to their utmost capacity. The contributions of artificial intelligence and data analytics cannot be forgotten. Several research outcomes are brought up with the use of machine learning (ML) and deep learning (DL) by analyzing worldwide COVID-19 related datasets. Various predictive analytics models have been proposed by statisticians, clinicians, and computer scientists. Now, this is the time to evaluate these models and to prove the validity of the proposed methods. This study aims to analyze the effectiveness of the predictive analytics models proposed during the pandemic by using ML and DL methods. In this review, the original research works put forth in the indexed journals during the pandemic period are analyzed and how they are relevant in today’s context. Besides, the featured algorithms widely used in various frameworks and their importance in predictive analytics are presented